Dothe RichStayUnemployedLonger? AnEmpirical Study for the UK 1 E l e n aG .F .S t a n c a n e l l i SZW Tilburg University and CLS Aarhus University September 1997 Abstract This paper investigates the impact of individual asset holdings on the probability of leaving unemployment. According to the theory, higher levels of ﬁnancial wealth will result in higher reservation wages and longer unemployment durations. I estimate the impact of beginning of period ﬁnancial assets on the hazard rate, using data drawn from a UK inﬂow sample of the unemployed. The empirical ﬁndingsindicate that individualasset holdingsaffect signiﬁcantlythe escape rate out ofunemployment. In particular,negative levels of wealthincrease signiﬁcantlythe hazardofleavingunemploymentwhilepositivelevels ofwealthreduce signiﬁcantlytheprobability of leaving unemployment. Keywords: duration analysis C41, unemployment duration J64. 1 Introduction Theliteratureontherelationshipbetweenﬁnancialassetsandlabourmarkettransi- tionsis very scarce. Assets play no rolein the conventional job search model. The assumptions of perfect capital markets and risk neutrality rule out any inﬂuence of ﬁnancial assets on the probability of leaving unemployment. Consequently, the job search literature has ignored the impact of asset holdings. However, the assumption of perfect capital markets appears rather restrictive. 1ThispaperdrawsonearlierresearchcarriedoutattheEuropeanUniversityInstituteinFlorence and at the Free Universityof Amsterdam. Earlier versions of thispaper were presented at CREST, Paris, February 1994, at CentER, Tilburg University, May 1995, at the World Meeting of the Econometric Society, Tokyo, August 1995 and at University College London, December 1995. I thank for useful comments John Micklewright, Robert Waldmann, Hans Bloemen and Arthur van Soest. John Micklewrightand Patrick Heady are to be thankedfor having made the data available. All errors are mine. 1Danforth (1979) showed that under the assumptions of utility maximization, decreasing absolute risk aversion, a limit to borrowing possibilities and a time preferencerateaslargeasthemarketdiscountrate,thefollowingthreepropositions hold (Danforth, 1979, p. 111): “the rich are more selective”, i. e. higher levels of wealth result in lower acceptance probabilities; “the rich search longer”, i. e. an increase in the level of wealth raises the expected duration of unemployment; “the rich get richer”, i. e. expected returns from search increase with in- creased search time. Surprisingly, the issue has been ignored in the subsequent literature with few exceptions. Blundell et al. (1997) specify a discrete dynamic model where individual choose each period their consumption level and their labour market status with endogenous job offer and lay off rates. The job offer arrival rate is higher (i. e. the retention rate is higher) for individuals in work. In the model, savings can be used to smooth consumption. The authors show that individuals with levels of savings above a given threshold will not work. The model is not tested empirically but conditions for identiﬁcation and data requirement are laid out. Financialassetsareabsentfrommostempiricalmodelsoflabourmarkettransi- tions. Bloemen (1994)estimates the impact of assets on labourmarket transitions, using data from the Dutch Socio-Economic Panel. He ﬁnds some evidence of a negative relationship between savings and the probabilityof becoming employed. Bloemen and Stancanelli (1997) investigate the relationship between ﬁnancial wealth, reservation wages and labour market transitions also using data from the Dutch Socio-Economic Panel. The authors conclude that individual wealth has a signiﬁcantly positive impact on the reservationwage and a negative impact on the employment probability. It is the purpose of this paper to test whether savings affect the hazard rate out of unemployment. One of the reason for the paucity of studies in this area is that models that explain simultaneously assets accumulation, consumption levels and labour market transitions are not easy to specify and to solve. Another reason is that data on assets are rarely reliable. The approach followed here will be of an explorative nature. The impact of beginning of periodassets on thehazard ratewill be estimated, adopting areduced 2form approach. The data used are an unexploited UK dataset which contains detailed information on the asset holdings of the unemployed, the Survey of the Standard of Living of the Unemployed (LSUS). The structure of the paper is the following. The data used for the analysis are described in Section 2. In Section 3, the model is speciﬁed. The results of estimation are discussed in Section 4. Conclusions are drawn in Section 5. 2 The data ThedatausedfortheanalysisaretheSurveyofLivingStandardsduringUnemploy- ment (LSUS). This is a longitudinal (inﬂow) sample of registered unemployment in Great Britain. The advantage of these data for the purpose of my analysis are the detailed questions asked on the ﬁnancial situation of the unemployed. The survey sample includes unemployed household heads, aged between 20 and 58 years, that entered unemployment in the summer of 1982 and stayed unemployed for at least three months 1. I select men only for the analysis. Two interviews were carried out with the sample participants: the ﬁrst three months after the start of their (registered) unemployment spell; the second about 15 monthslater (about ayear after theﬁrst survey interview). Theinterviewswere conducted personally by the interviewers at the home of the survey participants. Detailed information on savings, debt and other individual characteristics was collected at the two survey interviews. Retrospective questions on the ﬁnancial situation one month before the start of the unemployment spell were asked at the timeoftheﬁrstinterview. Respondentswereaskedto checktheirﬁnancialbalance statements. I use information on the level of ﬁnancial wealth before entry into unemploy- ment. The savings of the unemployed are set equal to the sum of the amounts of money held under the following forms: bank current accounts; deposit or savings accounts; building society accounts; stock shares or other securities; premium bonds; any other savings. End-of-job payments received at about the start of the unemployment spell include redundancy payments and severance payments. These payments are added to the total savings 2. The unemployed’s debt is com- 1As a consequence of this last selection criterion, the sample is left-truncated at about three months from the start of the unemployment spell. This is easily dealt with when writingdown the likelihoodfunction, as it is done in the next Section. 2Sensitivity analysis with respect to the exclusion of end of job payments from net wealth in the estimating model is carried out in Section 4 (see Table 5). 3puted summing up debt run under the following forms: money owed to friends or relatives; money owed to a money lender, a bank, a ﬁnance house, a credit card company, and any other person or organization; any arrears with mortgage pay- ments and any other payments. Net wealth is computed as the difference between savings and debt 3. The duration of the unemployment spell is constructed from retrospective questionsonthe economicactivityweek-by-weekintheyear betweentheﬁrst and the second interview, asked at the second interview. Therefore, only participants in both survey interviews are selected for the analysis. Attrition would only be a problem if non-response is associated with exit from unemployment. Some analysis of attrition excluded this possibility (Stancanelli, 1994). Unemployment beneﬁt is computed as the sum of unemployment insurance, unemployment assistance and housing beneﬁt. Housing beneﬁts are an important component of unemployment beneﬁts in the UK. I allow the level of beneﬁt to vary at the time of the exhaustion of unemployment insurance. I use information on the level of savings and on the spouse’s labor force participation to predict unemployment insurance exhaustion. These variables determine entitlement to social assistance beneﬁts in the UK 4. Theearningsvariablerelatestousual earningsinthejobheld onemonthbefore the start of the unemployment spell, if any. This informationwas not available for about 50% of therespondents. I use adummy to control forthe non-availabilityof the earnings information. Other income is equal to the sum of any other beneﬁts accruing to the unemployed or their spouse and the earnings of the spouse, if any. The occupation dummies take value one if the unemployed’s last job was respectively, in a “professional or intermediate” occupation or in an “unskilled” 3In the survey no information is available on the value of the house. However, informationon houseownershipoutrightorwitha mortgageiscollected. Therefore, I controlforhouseownership with a set of dummies. 4At the time the survey was carried out, savings above the threshold level of $3000 or a working spouse would prevent the unemployed from gaining entitlement to social assistance beneﬁts. Unemployment insurance beneﬁts lasted for 52 weeks. However, the maximum duration of entitlement to UB in a given spell of unemployment could actually be less than 52 weeks for some unemployed because of the so called “link spell” rule: unemployment spells separated by less than eightweeks of employment were counted togetheras singlespells for beneﬁt entitlement purposes. On the basis of retrospective information on the economic activity week by week in the year before entry into unemployment,collected at the time of the ﬁrst interview, I did actually conclude that 91.2% of thesample was not interested by the link spell rule (Stancanelli, 1994). In Stancanelli (1997a), it is concluded that there is no signiﬁcant expected beneﬁt exhaustion effect in the UK, using the same dataset. This effect is therefore not modelled in the current study. 4occupation. The base group for these dummies are the skilled and semi-skilled workers. I consider the following variables for age and family composition: age dummies, marital status dummy, a dummy for the presence of any child aged less than 5 in the nuclear family. The rate of unemployment in the local area (the county) is used to capture demand side conditions. At the time when the survey was carried out theaggregate unemploymentrateaveraged about 12-13% in Great Britain. Descriptive statistics of the explanatory variables are given in Table 1 for the full sample and for the unemployed that reported, respectively, positive wealth, negative wealth and zero wealth. The following points are worth noticing. Therearemoreindividualswithyoungchildren(lessthanﬁveyearsold)among the unemployed with negative or zero wealth. The number of house owners outright is higher among the unemployed with positive levels of wealth. The number of house owners with a mortgageis instead larger among the unemployed with negative or zero wealth. The number of working spouses is smaller among individualswithnegativeandzerowealth. Interestingly,theproportionofunskilled workers is higher among individuals with zero levels of wealth than among the unemployed with negative or positive wealth levels. Similarly, professional or intermediate occupations are less represented among the unemployed with zero levels of wealth than among those with positive or negative levels of wealth. The number of house owners is smaller among the unemployed with zero levels of wealth. Fewer of those individuals with zero levels of wealth were in work in the week before entering unemployment. Average earnings are lower for the unemployed with zero level of wealth while the proportion of individuals that did not report earnings stays more or less the same across the three groups. Overall, this evidence suggests that the unemployed with negative levels of wealth may have “better” characteristics than those with zero levels of wealth. Indeed, zero levels of net wealth, as opposed to negative wealth, may signal incapacity to borrow or to run debt. The unemployed with negative levels of wealth may have “better” characteristics than those with no wealth since they are able to run debt. In Table 2, I show some descriptive statistics of wealth for different groups of the unemployed. Individuals with professional or intermediate occupations 5 have a mean level of net wealth of $4700, about six times larger than that of the unskilledworkers($858). Houseownershaveameanlevelofnet wealthof$3173, sixtimesaslargeonaveragethanindividualslivinginrentedaccommodation. The 5The occupational group is constructed on the basis of the occupation in the last job. 5unemployedon social assistance haveanegativemean level of netwealth, equal to minus $178. Those on unemployment insurance with no social assistance 6 have a mean level ofnet wealth of about$4400. Individualsthatreceived someseverance orredundancy paymentshave alevel ofmean net wealthfourteentimeslargerthan that of the other unemployed. Individuals with (past) earnings above the mean sample earnings have average net wealth equal to $3675 while individuals with earnings below the mean have average net wealth equal to $1011. Persons older than the average age in the sample have a considerably higher mean level of net wealth ($3288) than younger persons ($114). 3 The empirical model The theoretical frameworkof the model is the theory of job search along the lines of the model put forward by Danforth (1979). The probability of leaving unemployment is equal to the product of the proba- bilities of receiving a job offer and accepting it. Offers are characterized in terms of the attached wage. The acceptance probability depends on the reservation wage, which is that wage at which individuals are indifferent between continuing to search or accepting the job offer. Danforth’s (1979) theoretical search model predicts a positive relationship between acceptance wages and asset holdings. Here the probability of leaving unemployment is modelled with the hazard rate. Wealth is measured at the beginning of the period. In particular, wealth is measured one month beforeentryinto unemploymenton thebasis of retrospective questions asked at the time of the ﬁrst interview. According to the theory, wealth is expected to have a negative impact on the hazard rate by raising the reservation wage. I model the hazard rate as a piecewise linear exponential. This is equivalent to the non-parametricspeciﬁcation adopted, for example, in Meyer (1990). Given some time periods, c m, the hazard rate can be written as: ( t ) = 8 > > > > > > < > > > > > > : 1 ; 0 t c 1 2 ; c 1 < t c 2 M ; c M 1 < t < 1 (1) 6In the UK, the unemployed may receive social assistance payments on top of unemployment insurance if theirresources includingunemploymentinsurance payments fallbelowtheir needs on the basis of means-tests. 6The baseline hazard speciﬁes the hazard rate as a function of the time spent in the state of unemployment. I allow the baseline hazard to vary every two weeks. This should allow enough ﬂexibility while at the same time ensuring that enough observations fall in each time period. Other covariates are allowed to affect the hazard of leaving unemployment by entering the hazard rate model as follows: ( t j x ) = e x p ( x ) e x p ( m ) ; (2) c m 1 t < c m m = 1 ; 2 ; ; M where is a vector of parameters and the x’s are covariates that are allowed to vary over time. In particular, the level of unemployment beneﬁts varies over time. The survivor function conditional on left-censoring is: G ( t j x ; s ) = e x p f X m ( m ; s ; t ) [ e x p ( x ) e x p ( m ) ] g (3) c m 1 t < c m m = 1 ; 2 ; ; M ( m ; s ; t ) = 8 > > > < > > > : t c m 1 i f s < c m 1 a n d c m 1 t < c m c m c m 1 i f s < c m 1 a n d t c m t s i f c m 1 s < c m a n d c m 1 t < c m 0 o t h e r w i s e (4) where s is the left truncation period, which is set equal to the time of the ﬁrst survey interview and varies in practise between 11 and 17 weeks. The likelihood function for the data is the following: L = Y i ( t i j x i ) d i Y i G ( t i j x i ; s i ) (5) where the “d” takes value one if individual i exits from unemployment and zero otherwise. Allowing for unobserved heterogeneity the hazard rate can be written as: ( t j x ; v ) = e x p ( x ) e x p ( m ) e x p ( v ) ; (6) c m 1 t < c m m = 1 ; 2 ; ; M 7and the survivor function conditional on left-censoring is: G ( t j x ; s ) = Z f e x p f Z t s ( u j x ; v ) d u g d H ( v j x ) (7) where H is the mixing distribution. I use a discrete ﬁnite mixing distribution. The likelihood contribution is equal to the density function for completed spells and the survivor functionfor right-censoredspells. The model has been estimated in Gauss. 4 The results of estimation I enter separately negative wealth, positive wealth and zero wealth. One would expect negative wealth to have a positive impact on the hazard rate and positive wealth to have a negative impact on the hazard rate. Individuals with zero net wealth should have stronger incentives to leave the pool of the unemployed. Non-parametric Kaplan-Meier estimates of the survivor functions for individ- uals with, respectively, positive, negative and zero levels of wealth are plotted in Figure1. The survivorfunctionforindividualswith zerolevel of wealthliesabove those of individuals with negative or positive levels of wealth. This suggests that individualswith zero levels of wealth have lower hazard rates at any point in time. The two survivor functions for the unemployed with negative levels of wealth or positivelevels of wealth are very close to each other. This result may be explained by the fact that non-parametric estimates do not control for individual (observed) characteristics. Results of estimation of the piecewise exponential hazard rate model are p- resented in Table 3. The preferred speciﬁcation is model (3) where unobserved heterogeneityiscontrolledfor. Model(2)isthesameasmodel(3)withoutcontrol- s for unobserved heterogeneity. The estimated parameters are larger in absolute values when unobserved heterogeneity is controlled for. Negative levels of net wealth have a signiﬁcantly positive effect on the hazard rate. In particular, an increase (in the absolute value) of negative net wealth of 100% raises the hazard of leaving unemployment by 5.7%, at the mean value of negative net wealth. Positive levels of net wealth have a signiﬁcantly negative effect on the hazard rate. In particular, an increase in positive net wealth of 100%, reduces the hazard of leaving unemployment by 10.9%, at the mean value of positive net wealth. The unemployed with zero levels of net wealth have signiﬁcantly lower chances of leaving unemployment. 8The other explanatory variables in the model have the expected sign and are mostly statistically signiﬁcant. Generally the results are in line with those of previous UK studies. In particular, my results compare reasonably with the ﬁndings of Arulampalam and Stewart (1995) who estimated the hazard of leaving unemploymentfortwodifferentcohortsofUKunemployed,surveyedrespectively in 1978 and 1987. In my preferred model (3), the elasticity of the hazard rate with respect to the levelofunemploymentbeneﬁtisequalto 0 : 0 1 2. Ifthebeneﬁtincreasesby100% the hazard of leaving unemployment will fall by 1%. This is a very small effect which is, however, probably explained by the high level of unemployment (about 12-13%) at the time the survey was carried out. Arulampalam and Stewart (1995) ﬁnd an insigniﬁcant beneﬁt effect for the 1987 cohort of the unemployed but a a signiﬁcant beneﬁt elasticity of 0 : 3 8 for the 1978 cohort. As the authors point out, these results are likely to be explained by the behaviour of the unemployment rate in the UK, which was very low in 1978 and started increasing thereafter. The local rate of unemployment has an estimated coefﬁcient of 0 : 0 2 9 which is very close to the estimated coefﬁcient in Arulampalam and Stewart (1995), 0 : 0 2 5 for the 1987 cohort and 0 : 0 2 1 for the 1978 cohort. The elasticity of the hazard with respect to the level of previous earning is 0 : 7 2. Arulampalam and Stewart (1995) ﬁnd an elasticity of 0 : 5 5 for the 1987 cohort and of 0 : 7 3 for the 1978 cohort. The estimated coefﬁcient on the dummy for no previous earnings indicates that individuals for whom previous earnings were not available have signiﬁcantly higher chances of leaving unemployment. This is explained by the fact that this dummy captures the situation of new entrants into the labour market. Unskilled workers and older workers have signiﬁcantly lower chances of leaving unemployment. The estimated baseline hazard from Model (3) is plotted in Figure 2. The baseline hazard starts at week 12 of the unemployment spell because of the left truncation of the sample. The behaviour in the ﬁrst piece of the baseline is inﬂuencedby thesmall number ofobservationsthat falland orexit in that duration interval 7. There is some evidence of positiveduration dependence. This pattern is in line with the ﬁndings of Arulampalam and Stewart (1995). The hazard rate for the1978 cohort showssome positivedurationdependencefrom week 10 onwards. The hazard rate forthe 1987 cohort is quitesmooth over time—and in this respect 7The reader is reminded that because of the sample design individuals with unemployment spells shorter than about 3 months (the time of the ﬁrst sample interview) were dropped from the sample. Individualleft truncationperiods vary between 11 and 17 weeks. 9more similar to the hazard rate for the LSUS sample— and it shows some slightly negative duration dependence until about week 40 of unemployment. For both 1978 and 1987 cohorts there is evidence of positive duration dependence from week 40 onwards. A quadratic speciﬁcation in negative and positive levels of wealth (Model 1 of Table 3) was rejected against a linear speciﬁcation ( 2 2 = 1 : 1 7 2). Splinefunctions innet wealthhave also beenestimated withdifferentcutoff points, at the quartiles, at zero, negative and positive levels of net wealth. Under all speciﬁcations of the spline function, the coefﬁcient on the last segment of the spline was always signiﬁcantly negative. The last segment of the spline function includes in all speciﬁcations (net) savings above the threshold level that regulates entitlement to social assistance beneﬁts. However, since the results of estimation were generally very sensitive to the choice of cutoff points, my preferred speciﬁcation remains model (3). Excluding end of job payments from (positive) net wealth (case (a) Table 5) does not change substantially the results of estimation. The estimated coefﬁcients on negative and positive wealth are still signiﬁcant and have the expected sign. However, they are slightly smaller in absolute value. The same conclusions can be drawn if also the dummy for the receipt of any redundancy or severance pay is dropped from the model (case (b) Table 5) —the end-of-job payments dummy was not signiﬁcant in either models (2) or (3). Not much changes either when end-of-job payments are entered in levels in addition to being included in net wealth (case (c) Table 5). Dropping the dummy for the presence of a working spouseinthehousehold(case(d)Table5)hasalmostno effectontheestimated net wealth parameters. Adding controls for the receipt of social assistance payments (case (e) Table 5) leaves the estimated effect of net wealth on the hazard almost unchanged. In this last case, the inclusion in the model of a dummy for the receipt ofsocial assistancebeneﬁtscannotberejectedonthebasisofalikelihoodratiotest ( 2 1 = 1 1 : 3 4). However, this variable is not included in the preferred model since the UK literature on unemployment duration has opted for ignoring this issue 8. On the basis of the estimated parameters from model (3), I have computed predictedmeanunemploymentdurationsfordifferentlevelsofnetwealth(Table4). In the simulations carried out the explanatory variables are set equal to their mean and only the level of wealth is allowed to vary. The expected mean duration of unemployment is the shortest for the unemployed with net wealth levels equal to themean ofnegativenetwealth. Instead, theexpected meandurationisthelongest 8An exception is Stancanelli (1997b) to which the reader is referred for more details. 10for the unemployed with zero wealth. Increasing by 100% the level of wealth at the mean of positive net wealth increases the duration of the unemployment spell by one week. Increasing by 100% the level of wealth at the sample mean of net wealth increases the duration of the unemployment spell by half a week. 5 Conclusions In this paper, I have investigated the impact of asset holdings on the individual probability of leaving unemployment. The literature on the issue is scarce. The theory (Danforth, 1979) predicts that higher levels of ﬁnancial wealth will result in higher reservation wages and longer unemployment durations. The approach followed in this paper is empirical and of a reduced form nature. Oneoftheinterestingfeaturesofthispaperisthatuseismadeofanunexploited UK dataset which contains detailed information on the asset holdings of the unemployed, the Survey of the Standard of Living of the Unemployed (LSUS). In particular,thesedatarelatetoaninﬂowsampleoftheunemployed. Thisallowsme to estimate the impact of ﬁnancial wealth on the hazard rate. Wealth is measured at the beginning of the period. In the sample, individuals with professional or intermediate occupations have amean level ofnet wealth aboutsix timeslarger thanthat oftheunskilled workers. Houseownershaveameanlevelofnetwealthsixtimesaslargeonaveragethanthat of individuals living in rented accommodation. The unemployed receiving social assistance have a negative mean level of net wealth. Those on unemployment insurance with no social assistance have a mean level of net wealth of about 4400 $. Individuals that receive some severance or redundancy payments have a level of mean net wealth fourteen times larger than that of the other unemployed. Ienterseparatelynegativewealth,positivewealthandzerowealthinthehazard rate model. According to the theory, wealth is expected to have a negative impact on the hazard rate. Therefore, one would expect negativewealth to have a positive impact on the hazard rate and positive wealth to have a negative impact on the hazard rate. Individuals with zero net wealth should have stronger incentives to leave the pool of the unemployed. On the basis of the estimation of the model, the following conclusions can be drawn. Negative levels of net wealth have a signiﬁcantly positive effect on the hazard rate, as expected. In particular, an increase (in the absolute value) of negative net wealth of 100% will increase the hazard of leaving unemployment by 5.7%, at the mean value of negative net wealth. Positive levels of net wealth 11have a signiﬁcantly negative effect on the hazard rate. An increase in the level of positive net wealth of 100%, will reduce the hazard of leaving unemployment by 10.9%, at the mean value of positive net wealth. Zero net wealth is found to reduce considerably the hazard rate of leaving unemployment. This is likely to be explained by the fact that the unemployed that are not able to borrow or to save may have negative individual characteristics which are also associated with low offer probabilities. Indeed, the proportion of unskilled worker is twice as large among the unemployed with zero levels of wealth than among the unemployed with positive or negative wealth and previous wages are on average lower for the unemployed with no wealth. References ATKINSON,A .B .AND MICKLEWRIGHT J. (1991), “Unemployment Compensation and Labour Market Transitions: A Critical Review”, Journal of Economic Literature, XXIX, 1679– 1727. ARULAMPALAM,W .AND STEWART, M. (1995), “The DeterminantsofIndividualUnemployment in an Era of High Unemployment: Are the Inﬂuences Different?”, Economic Journal, 105, March.333–344 BLOEMEN, H. G. (1994), “Estimating a Model of Savings and Labor Market Transitions with Dutch data”, CentER, Tilburg University, mimeo. BLOEMEN,H .G .AND STANCANELLI, E.G.F. (1996),“IndividualWealth, ReservationWages and Transitions into Employment”, CentER, DP No. 9702, Tilburg University. BLUNDELL,R .,M AGNAC,T .AND MEGHIR C. (1997),“Savingsand Labour MarketTransitions”, Journal of Business and Economic Statistics,15, 2.153–163 DANFORTH, J. P. , (1979),“On theRoleofConsumptionand Decreasing AbsoluteRiskAversion in the Theory of Job Search”, in: LIPPMAN,S .A .AND MCCALL J. J. (1979),Studies in the Economics of search, North-Holland. LANCASTER, T.(1990),TheEconometricAnalysisofTransitionData,CambridgeMassachusetts University Press. RIDDER, G. (1987), “The Sensitivity of Duration Models to Misspeciﬁed Unobserved Hetero- geneity and Duration Dependence”, University of Amsterdam, mimeo. STANCANELLI, E. G. F. (1997A), “Unemployment Duration and the Duration of Entitlement to Unemployment Beneﬁts: an Empirical Study for Britain”, Applied Economics, forthcom- ing. STANCANELLI, E. G. F. (1997B), “Unemployment Compensation Schemes and Unemployment Duration”, Applied Economics Letters, forthcoming. 12STANCANELLI, E. G. F. (1994), “The Probability of Leaving Unemployment: Some New Evi- dence for Great Britain”, PHD thesis, European University Institute,Florence. 13Table 1: Descriptive Statistics Variable All Net Wealth > 0 Net Wealth < 0 Neth Wealth = 0 Mean (SD) Mean (SD) Mean (SD) Mean (SD) Left Truncation 13.40 (1.05) 13.39 (1.03) 13.39 (1.02) 13.46 (1.14) U durationweeks 47.21 (19.00) 46.20 (19.05) 47.07 (19.12) 50.01 (18.48) U duration*weeks 34.33 (14.62) 34.54 (14.83) 34.01 (14.50) 34.36 (14.22) Completed Spell 0.59 (0.49) 0.62 (0.48) 0.59 (0.49) 0.50 (0.50) Prof-Int Occ 0.17 (.37) 0.20 (0.40) 0.16 (0.37) 0.09 (0.29) Unskilled Occ 0.06 (0.24) 0.05 (0.22) 0.05 (0.21) 0.10 (0.30) Age 20–24 0.13 (.33) 0.08 (0.28) 0.16 (0.36) 0.18 (0.39) Age 25-34 0.33 (0.47) 0.27 (0.45) 0.40 (0.49) 0.35 (0.48) Age 35-44 0.25 (0.43) 0.23 (0.42) 0.27 (0.44) 0.26 (0.44) Age 45-54 0.19 (0.39) 0.24 (0.43) 0.15 (0.35) 0.15 (0.36) Age 55-58 0.11 (0.31) 0.18 (0.38) 0.03 (0.16) 0.05 (0.22) Married 0.86 (0.34) 0.86 (0.35) 0.89 (0.31) 0.83 (0.38) Spouseworks 0.25 (0.43) 0.32 (0.47) 0.22 (0.41) 0.14 (0.34) Any Child Aged < 5 0.35 (0.48) 0.27 (0.44) 0.44 (0.50) 0.41 (0.49) Savings,in 100 $ 13.62 (56.25) 27.39 (78.46) 1.08 (5.32) 0.08 (0.92) Debt, in 100 $ 5.98 (27.35) 2.04 (9.18) 15.32 (45.28) 0.09 (1.12) End-of-Jobs Payments, in 100 $ 7.90 (31.81) 16.25 (44.24) 0.13 (1.09) 0.01 (0.26) Net Wealth, in 100 $ 15.54 (72.96) 41.60 (91.24) -14.11 (44.37) 0.00 (0.00) Other HH Income,in $ 21.81 (25.09) 22.79 (27.06) 21.83 (23.45) 19.28 (22.34) House Owner 0.37 (0.48) 0.47 (0.50) 0.34 (0.48) 0.17 (0.38) House with mortgage 0.34 (0.47) 0.24 (0.43) 0.43 (0.50) 0.46 (0.50) County U Rate 0.14 (0.03) 0.13 (0.03) 0.14 (0.03) 0.14 (0.03) Beneﬁt, in logs,$ 3.70 (0.42) 3.64 (0.40) 3.77 (0.43) 3.72 (0.42) Earnings,in logs, $ 2.42 (2.25) 2.89 (2.21) 2.10 (2.23) 1.77 (2.15) Earnings*,in logs, $ 4.48 (0.45) 4.55 (0.41) 4.41 (0.47) 4.41 (0.47) Earnings dummy 0.46 (0.50) 0.36 (0.48) 0.52 (0.50) 0.59 (0.49) In work week before 0.60 (0.49) 0.63 (0.48) 0.61 (0.49) 0.49 (0.50) No Observations 1941 939 630 372 The Net Wealth variable is computed by summming savings to end of job payments and subtracting from that debt. The dummy variables take value one when the condition stated for each of them is satisﬁed. The earnings dummies take value one when the earnings level is not available. The ﬁrst mean duration includes the right-censoredobservations. The second mean duration (*) is computed only over the completed spells. The logarithms are taken over the non-zero observations. “Earnings*” indicates that the mean is computed for the non-zero earnings. The “spouse works” dummy relates to one month before entry into unemployment. 14Table 2: Mean Net Wealth for Different Groups Group Cases No. Mean SD Median 25% 75% Sample 1941 15.54 72.96 0.0 -1.22 10.00 Profess-Interm Occ 323 47.30 145.49 3.0 -2.35 38.69 Unskilled Occ 117 8.58 33.10 0.00 -0.05 2.04 Past Earn. > MeanPast Earn. 362 36.75 109.31 5.02 0.00 35.12 Past Earn. < = Mean Past Earn. 688 10.11 31.83 0.01 -1.20 7.53 Age > Mean Age 881 32.88 100.75 3.20 0 34.00 Age < = Mean Age 1060 1.14 29.32 0 -2.04 1.60 No End Job Pay 1610 4.90 52.37 0.0 -2.08 2.00 End of Job Pay 331 67.30 121.18 29.28 12.12 73.83 House Owner 724 31.73 110.21 2.77 -1.50 30.18 Not House Owner 1217 5.91 31.99 0.00 -1.20 3.60 Social Assistance 1205 -1.78 35.10 0 -2.22 1.57 U Insuranceonly 736 43.91 103.61 7.00 0 46.10 Married 1677 15.62 72.16 0.00 -1.38 10.00 Not Married 264 15.07 78.03 0 -0.20 10.20 Spouse Works 488 21.22 70.11 2.15 -1.04 24.91 Spouse Does Not Work 1453 13.64 73.82 0.00 -1.26 5.57 Net Wealth is measuredin 100$. 15Figure 1: 16Table 3: Results of Estimation Variable Model(1) Model(2) Model(3) Name Coeff SE Coeff SE Coeff SE Unskilled Occup. -0.3842 * 0.1548 -0.3827* 0.1550 -0.6457* 0.2255 Age 30–34 -0.1487 0.0986 -0.1480 0.0986 -0.1983 0.1344 Age 35-44 -0.2122 * 0.1074 -0.2144* 0.1075 -0.2667** 0.1437 Age 45-54 -0.5795 * 0.1203 -0.5858* 0.1202 -0.8001* 0.1650 Age 55-58 -0.9466 * 0.1535 -0.9680* 0.1511 -1.3637* 0.2148 Has any child -0.2010 * 0.0799 -0.1988* 0.0798 -0.2933* 0.1095 Married 0.0550 0.1079 0.0543 0.1079 0.1362 0.1505 House owner 0.2528 * 0.0682 0.2478* 0.0680 0.3400* 0.0938 Earnings logs, $ 0.5235 * 0.0918 0.5167* 0.0917 0.7173* 0.1208 Prof.-Interm. Occup. 0.3092 * 0.0811 0.3038* 0.0809 0.4299* 0.1089 County U rate -0.0202 * 0.0095 -0.0202* 0.0095 -0.0295* 0.0130 Earn. dummy 2.3554 * 0.4172 2.3221* 0.4167 3.2463* 0.5508 Neg Net Wealth 0.4726 0.3112 0.3432* 0.1526 0.4091* 0.1839 Neg Net Wealth Squared 0.0357 0.0887 Pos Net Wealth -0.2776 * 0.1178 -0.1951* 0.0602 -0.2622* 0.0811 Pos Net Wealth Squared 0.0152 0.0169 No End-of-Job Pay 0.0740 0.0969 0.0880 0.0944 0.0952 0.1268 Zero Net Wealth -0.2781 * 0.0844 -0.2690* 0.0837 -0.3695* 0.1194 Had Job Week Before 0.1507 * 0.0763 0.1528* 0.0762 0.1917** 0.1052 Spouse Works Month Before 0.2954 * 0.0932 0.2968* 0.0932 0.4347* 0.1252 Has Mortgage 0.2000 * 0.0667 0.2009* 0.0667 0.3223* 0.0919 Other HouseholdIncome 0.0015 0.0015 0.0015 0.0015 0.0011 0.0020 Log U Beneﬁt, tim var -0.0079* 0.0026 -0.0078* 0.0026 -0.0121* 0.0034 Mass point* 2.3265* 0.2161 Parameter P* 1.0730* 0.2102 Log-likelihood -5443.22 -5443.73 -5432.35 The baseline hazard involves 27 parameters. The number of observations is 1941. Net Wealth is measured in 10000$. Italsoincludeendofjobpayments. Model(3)includescontrolsforunobservedheterogeneitybymeansof mass points. Two mass points havebeen introduced. The ﬁrstis normalized to zero ( v a = 0 , e x p ( v a ) = 1 ). The probabilitydistributionofthemasspointsisspeciﬁedas: p v a = 1 = ( 1 + e x p ( P ) ); p v b = e x p ( P ) = ( 1 + e x p ( P ) ). 17Figure 2: 18Table 4: Predicted Mean Unemployment Duration Assumptions made Predicted Duration Net Wealth not included in the hazardrate model 33.90 Net Wealth equal to zero 38.22 Net Wealth equal to sample mean 34.42 Net Wealth equal to twice the sample mean 34.94 Net Wealth equal to half the sample mean 34.16 Net Wealth equal to mean value of positive net wealth 35.29 Net Wealth equal to twice the mean value of positive net wealth 36.58 Net Wealth equal to mean value of negative net wealth 33.12 The base model is model (2). The mean expected duration has been computed as E ( U m e a n ) = P 6 2 t = 1 2 G m e a n t where G m e a n t istheexpectedmeansurvivorfunction, evaluatedat the mean values of the explanatoryvariables. Table 5: Some Robustness Checks Model Restriction/Extension Neg Net W Pos Net W No Net W No End-Job-Pay Log-Lik Base Model 0.34 (0.15) -0.19 (0.06) -0.27 (0.08) 0.09 (0.09) -5443.73 (a) End Job Pay excluded from wealth 0.31 (0.15) -0.15 (0.07) -0.27 (0.08) 0.16 (0.09) -5446.72 (b) End Job Pay excluded from wealth and end of job pay dummy droppedfrom the model 0.31 (0.15) -0.15 (0.07) -0.26 (0.08) -5448.23 (c)EndofJobPayis additionallyen- teredinlevels( 0 : 2 4 ; 0 : 1 4)andthe Dummy is droppedfrom the model 0.34 ( 0.15) -0.14 (0.07) -0.26 (0.08) -5442.80 (d) Spouse Works Dummy dropped fromthe model 0.33 ( 0.15) -0.21 (0.06) -0.28 (0.08) 0.07 (0.09) -5448.72 (e) Dummy for the receipt of So- cialAssistance Beneﬁtsaddedto the model(-0.26, 0.08) 0.31 ( 0.15) -0.21 (0.06) -0.26 (0.08) 0.10 (0.09) -5438.06 The base model is the preferredmodel, model(3). 19

Do the Rich Stay Unemployed Longer? An Empirical Study For the U.K

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